Quantization and Feedback of Spatial Covariance Matrix for Massive MIMO Systems with Cascaded Precoding

TL;DR

This paper proposes a novel spatial spectrum-based method for quantizing and feedback of the large spatial covariance matrix in massive MIMO systems with cascaded precoding, reducing overhead and leakage.

Contribution

It introduces a new approach using spatial spectrum for covariance matrix quantization, improving efficiency over traditional methods.

Findings

Achieves smaller spatial leakage compared to DFT-based precoding.

Reduces feedback overhead in massive MIMO systems.

Addresses practical implementation issues.

Abstract

In this paper, we investigate the quantization and the feedback of downlink spatial covariance matrix for massive multiple-input multiple-output (MIMO) systems with cascaded precoding. Massive MIMO has gained a lot of attention recently because of its ability to significantly improve the network performance. To reduce the overhead of downlink channel estimation and uplink feedback in frequency-division duplex massive MIMO systems, cascaded precoding has been proposed, where the outer precoder is implemented using traditional limited feedback while the inner precoder is determined by the spatial covariance matrix of the channels. In massive MIMO systems, it is difficult to quantize the spatial covariance matrix because of its large size caused by the huge number of antennas. In this paper, we propose a spatial spectrum based approach for the quantization and the feedback of the spatial covariance matrix. The proposed inner precoder can be viewed as modulated discrete prolate spheroidal sequences and thus achieve much smaller spatial leakage than the traditional discrete Fourier transform submatrix based precoding. Practical issues for the application of the proposed approach are also addressed in this paper.